Delineation of the tumour boundary and assessment of tumour size are needed for patient management in terms of treatment planning and monitoring treatment response. Current clinical guidelines incorporate the use of both T1-contrast images and T2-weighted/FLAIR images (Niyazi et al., 2016; Wen et al., 2010). As explained in Chapter 2, each MRI protocol presents specific characteristics of the brain or tumour tissue. Many low-grade gliomas do not show contrast enhancement hence T2-weighted/FLAIR images are used to define the tumour extent and volume. T2-weighted/FLAIR images can also be useful to help define the target volumes for radiotherapy planning of high-grade gliomas (Aslian et al., 2013; Niyazi et al., 2016). From a technical point of view, using a single protocol as input to solve a binary class segmentation problem decreases the complexity of the model due to less data, smaller feature dimensionality and no need for image registration. FLAIR is considered for this task as it has been in routine clinical use as part of standard diagnosis of brain tumours. Delineation of the FLAIR hyperintensity is important to assess low-grade glioma growth (Law et al., 2008), define an abnormal region from which imaging features for tumour classification can be extracted (Itakura et al., 2015), aid with radiation dose planning (Stall et al., 2010) and assess the treatment responses (Cho et al., 2012). Detecting the complete tumour in the image can be considered as an initial stage which can be further used for tumour component segmentation tasks.
The motivation of this chapter is to use a single modality approach to detect the complete tumour structure in the clinical data. To assess the robustness of the proposed method, it is also evaluated on the FLAIR protocol of BRATS 2013 annotated training dataset.
As mentioned in Chapter 3, the methods in (Pinto et al., 2015) and (Gotz et al., 2014) calculated the image features based on each individual voxel, which a fixed size neighbourhood around the voxel is considered for the feature extraction. In this chapter, instead of using a fixed size neighbourhood, a patch based method using superpixel partitioning is investigated for feature extraction and the final segmentation is directly obtained from the superpixel’s boundary. This will ensure that the patches are more separable for the classifier, since the pixels inside the patch have more similarity compared to the fixed sized window suggested in (Pinto et al., 2015) and (Gotz et al., 2014). It will also increase the
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computational speed of the feature extraction and classification stages compared to the pixel- wise based approaches which require the corresponding procedures to be performed on all the pixels within the input image.
The idea of using superpixel instead of the pixel-level calculation have been used for object classification in the images. Fulkerson et al. (Fulkerson et al., 2009) proposed using the superpixel patches of images instead of pixel level classification. They extracted bags of features for each superpixel and classify them using SVM. In this thesis, one of the contributions including the histogram of textons in the bag of features that is extracted from the superpixels.
Yi and Sun (Yi and Su, 2014) used the histogram of Gabor filter responses as the representation of the features. They suggested that using log-Gabor filters will reduce dimensionality and the computation cost. Fixed-sized non-overlapping blocks of the images was used in their method to calculate the histogram of Gabor histogram. In this thesis, flexible superpixel patches are used instead of the fixed blocks. The flexible boundaries of the superpixels create non-overlapping patches that adhere to the image edges.
Yu et al. (Yu et al., 2012) used the bag of textons and superpixel for unsupervised image segmentation. The texton filter bank were comprised of 2D Gaussian filters. In this thesis Gabor filters are used which represent more description of spatial and frequency features. In this chapter, a fully-automated superpixel based method will be investigated for detection and segmentation of the abnormal tissue associated with brain tumours, as defined by the hyperintensity from FLAIR MRI. In the proposed method, superpixel partitions are firstly calculated to provide accurate boundaries between different tissues. Several non-parametric and hand designed image features are then extracted from each superpixel. This will improve the accuracy of feature calculation and increase the computation speed. The superpixels are then classified using the state-of-the-art ERT which is a powerful classifier that can deal with high dimensional features and large-sized unbalanced data.
A texton is considered as a texture feature for the whole image segmentation. The idea is to use the texton histogram of a specific superpixel (which is obtained from the whole image texton map) as the main feature for that superpixel. This will be discussed in this chapter. The contribution of this chapter can be listed as follows:
Investigation of an automated method that provides a close match to expert delineation across all grades of glioma using the single commonly used MRI modality i.e. FLAIR.
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The method could provide a faster segmentation (approximately three times faster) of brain tumours, and good agreement with the human expert delineation .
Extraction of powerful hand designed features from superpixels instead of common pixel-wise feature extraction, with focus on the state-of-the-art texton features.
Applying ERT directly to the superpixels instead of all the voxels (Gotz et al., 2014; Pinto et al., 2015), which largely reduces the data size for classification. Superpixels with the same classification label are grouped together, and are considered as the tissue ROI.